Modeling Phase Transitions in Gene Expression State Space

描述（由申请人提供）：生物学中成功的定量方法包括建立详细的局部模型或检测高通量数据中的稳健信号。在该提案中，这两种方法以创新的方式结合起来，研究致癌病毒感染后人体组织的转录变化。此类病毒可能会产生一系列后果，从细胞表型的微小变化到剧烈转变。从包含有关病毒-宿主相互作用的所有已知信息的种子网络开始，将根据基因表达数据学习贝叶斯转录网络。然后，贝叶斯网络被转换为相互作用的电磁自旋的等效系统。这种自旋系统的例子已经在统计物理学中得到了研究，并且已知它们具有丰富的相结构。将模拟与宿主细胞网络相对应的自旋系统，并且对齐自旋的域将被识别为表征细胞对扰动的响应的遗传模块。这些模块的激活水平将用于划分基因表达状态空间中的阶段。以这种方式发现的新相和相变将通过实验进行验证。该框架从嘈杂的高吞吐量数据中筛选出概率交互，然后根据生成的网络模型做出新颖的预测。它是一种新的、定量的、具有生物学信息的方法来模拟人体细胞的扰动。在临床水平上，它可用于精细地区分患者的各种正常状态和疾病状态，并计算哪种疗法最能逆转疾病的进展。该技术有望使医疗诊断和治疗更加高效、定向和精准。 公共健康相关性：我的研究项目的目标是量化人类转录网络的扰动如何导致不同表型之间的转变。在这个框架下，临床医生将能够使用广泛使用的高通量方法来检测疾病状态。然后，他们可以确定个性化治疗或治疗组合，以最有效地逆转特定患者的疾病进展。

DESCRIPTION (provided by applicant): Successful quantitative approaches in biology have included building detailed local models or detecting robust signals in high-throughput data. In this proposal, both these methods are combined in an innovative way to study transcriptional changes in human tissue upon infection by oncogenic viruses. Such viruses can have a range of consequences, from minor changes to drastic transformations in the cell phenotype. Starting from a seed network consisting of all known information about the viral-host interaction, a Bayesian transcriptional network will be learned on the gene expression data. The Bayesian network is then transformed into an equivalent system of interacting electromagnetic spins. Examples of such spin systems have been studied in statistical physics, and they are known to have rich phase structures. The spin system corresponding to the host cell network will be simulated, and domains of aligned spins will be identified as genetic modules that characterize the response of the cell to perturbations. The activation levels of these modules will be used to demarcate phases in the gene expression state space. Novel phases and phase transitions discovered in this way will then be validated by experiments. This framework sifts out probabilistic interactions from noisy high- throughput data and then makes novel predictions based on the resulting network model. It is a new, quantitative, and biologically informative way to model perturbations to human cells. On a clinical level, it could be used to finely differentiate between various normal and disease states in patients, and to calculate which therapies would best reverse the progression of a disease. This technique has the potential to make medical diagnosis and treatment more efficient, directed and precise. PUBLIC HEALTH RELEVANCE: The goal of my research project is to quantify how perturbations to the human transcriptional network cause transitions between different phenotypes. Working in this framework, clinicians will be able to detect disease states using widely available high-throughput methods. They can then determine the personalized treatment, or combination of treatments, that will most efficiently reverse disease progression in a particular patient.